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Abstract
Variables such as average tree diameter at breast height (d.b.h.) and total height are important stand characteristics for forest management. Conventionally, tree d.b.h. and height are measured; however, due to the difficulty of measuring tree height in the field, this variable is often estimated from tree d.b.h. Recent developments in remote sensing techniques, in particular the application of light detection and ranging (LiDAR) technology to forestry, provides a promising alternative. LiDAR point clouds can be used to accurately estimate tree total height and crown area, which can be used to calculate the number of trees per unit area. This study used LiDAR data-derived stand variables including total height, crown size, and the number of trees per acre collected from permanent growth and yield plots established in loblolly pine (
Pinus taeda) plantations in east Texas to develop alternative models to estimate stand average d.b.h. The models were evaluated based on the mean difference between observed and predicted d.b.h., mean absolute difference, and root mean square error, respectively. Results indicate that stand-level d.b.h. could be accurately estimated using derived stand variables. The estimated stand d.b.h., paired with LiDAR-derived height and number of trees per acre, can then be employed to estimate stand-level volumes.
Parent Publication
Citation
Talmage, Clinton; Weng, Yuhui; Zhang, Yanli; Grogan, Jason. 2024. Diameter Estimation Based on LiDAR Point Clouds at Stand Level of Loblolly Pine Plantations. In: Bragg, Don C.; Oswald, Brian P.; Koerth, Nancy E., eds. Proceedings of the 22nd biennial southern silvicultural research conference. Gen. Tech. Rep. SRS-274. Asheville, NC: U.S. Department of Agriculture, Forest Service, Southern Research Station: 44–50. https://doi.org/10.2737/SRS-GTR-274-Pap7.
